An Exploration of Pepino (<i>Solanum muricatum</i>) Flavor Compounds Using Machine Learning Combined with Metabolomics and Sensory Evaluation
Flavor is one of the most important characteristics that directly determines the popularity of a food. Moreover, the flavor of fruits is determined by the interaction of multiple metabolic components. Pepino, an emerging horticultural crop, is popular for its unique melon-like flavor. We analyzed me...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/11/20/3248 |
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| author | Zhu Sun Wenwen Zhao Yaping Li Cheng Si Xuemei Sun Qiwen Zhong Shipeng Yang |
| author_facet | Zhu Sun Wenwen Zhao Yaping Li Cheng Si Xuemei Sun Qiwen Zhong Shipeng Yang |
| author_sort | Zhu Sun |
| collection | DOAJ |
| description | Flavor is one of the most important characteristics that directly determines the popularity of a food. Moreover, the flavor of fruits is determined by the interaction of multiple metabolic components. Pepino, an emerging horticultural crop, is popular for its unique melon-like flavor. We analyzed metabolomics data from three different pepino growing regions in Haidong, Wuwei, and Jiuquan and counted the status of sweetness, acidity, flavor, and overall liking ratings of pepino fruit in these three regions by sensory panels. The metabolomics and flavor ratings were also integrated and analyzed using statistical and machine learning models, which in turn predicted the sensory panel ratings of consumers based on the chemical composition of the fruit. The results showed that pepino fruit produced in the Jiuquan region received the highest ratings in sweetness, flavor intensity, and liking, and the results with the highest contribution based on sensory evaluation showed that nucleotides and derivatives, phenolic acids, amino acids and derivatives, saccharides, and alcohols were rated in sweetness (74.40%), acidity (51.57%), flavor (56.41%), and likability (33.73%) dominated. We employed 14 machine learning strategies trained on the discovery samples to accurately predict the outcome of sweetness, sourness, flavor, and liking in the replication samples. The Radial Sigma SVM model predicted with better accuracy than the other machine learning models. Then we used the machine learning models to determine which metabolites influenced both pepino flavor and consumer preference. A total of 27 metabolites most important for pepino flavor attributes to distinguish pepino originating from three regions were screened. Substances such as <i>N</i>-acetylhistamine, arginine, and caffeic acid can enhance pepino‘s flavor intensity, and metabolites such as glycerol 3-phosphate, aconitic acid, and sucrose all acted as important variables in explaining the liking preference. While glycolic acid and orthophosphate inhibit sweetness and enhance sourness, sucrose has the opposite effect. Machine learning can identify the types of metabolites that influence fruit flavor by linking metabolomics of fruit with sensory evaluation among consumers, which conduces breeders to incorporate fruit flavor as a trait earlier in the breeding process, making it possible to select and release fruit with more flavor. |
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| institution | Directory Open Access Journal |
| issn | 2304-8158 |
| language | English |
| last_indexed | 2024-03-09T03:39:37Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
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| series | Foods |
| spelling | doaj.art-96e0a214928343469e98f826ad5b55f82023-12-03T14:43:55ZengMDPI AGFoods2304-81582022-10-011120324810.3390/foods11203248An Exploration of Pepino (<i>Solanum muricatum</i>) Flavor Compounds Using Machine Learning Combined with Metabolomics and Sensory EvaluationZhu Sun0Wenwen Zhao1Yaping Li2Cheng Si3Xuemei Sun4Qiwen Zhong5Shipeng Yang6Qinghai Key Laboratory of Vegetable Genetics and Physiology, Agriculture and Forestry Sciences Institute, Qinghai University, Xining 810016, ChinaQinghai Key Laboratory of Vegetable Genetics and Physiology, Agriculture and Forestry Sciences Institute, Qinghai University, Xining 810016, ChinaQinghai Key Laboratory of Vegetable Genetics and Physiology, Agriculture and Forestry Sciences Institute, Qinghai University, Xining 810016, ChinaQinghai Key Laboratory of Vegetable Genetics and Physiology, Agriculture and Forestry Sciences Institute, Qinghai University, Xining 810016, ChinaQinghai Key Laboratory of Vegetable Genetics and Physiology, Agriculture and Forestry Sciences Institute, Qinghai University, Xining 810016, ChinaQinghai Key Laboratory of Vegetable Genetics and Physiology, Agriculture and Forestry Sciences Institute, Qinghai University, Xining 810016, ChinaQinghai Key Laboratory of Vegetable Genetics and Physiology, Agriculture and Forestry Sciences Institute, Qinghai University, Xining 810016, ChinaFlavor is one of the most important characteristics that directly determines the popularity of a food. Moreover, the flavor of fruits is determined by the interaction of multiple metabolic components. Pepino, an emerging horticultural crop, is popular for its unique melon-like flavor. We analyzed metabolomics data from three different pepino growing regions in Haidong, Wuwei, and Jiuquan and counted the status of sweetness, acidity, flavor, and overall liking ratings of pepino fruit in these three regions by sensory panels. The metabolomics and flavor ratings were also integrated and analyzed using statistical and machine learning models, which in turn predicted the sensory panel ratings of consumers based on the chemical composition of the fruit. The results showed that pepino fruit produced in the Jiuquan region received the highest ratings in sweetness, flavor intensity, and liking, and the results with the highest contribution based on sensory evaluation showed that nucleotides and derivatives, phenolic acids, amino acids and derivatives, saccharides, and alcohols were rated in sweetness (74.40%), acidity (51.57%), flavor (56.41%), and likability (33.73%) dominated. We employed 14 machine learning strategies trained on the discovery samples to accurately predict the outcome of sweetness, sourness, flavor, and liking in the replication samples. The Radial Sigma SVM model predicted with better accuracy than the other machine learning models. Then we used the machine learning models to determine which metabolites influenced both pepino flavor and consumer preference. A total of 27 metabolites most important for pepino flavor attributes to distinguish pepino originating from three regions were screened. Substances such as <i>N</i>-acetylhistamine, arginine, and caffeic acid can enhance pepino‘s flavor intensity, and metabolites such as glycerol 3-phosphate, aconitic acid, and sucrose all acted as important variables in explaining the liking preference. While glycolic acid and orthophosphate inhibit sweetness and enhance sourness, sucrose has the opposite effect. Machine learning can identify the types of metabolites that influence fruit flavor by linking metabolomics of fruit with sensory evaluation among consumers, which conduces breeders to incorporate fruit flavor as a trait earlier in the breeding process, making it possible to select and release fruit with more flavor.https://www.mdpi.com/2304-8158/11/20/3248fruit flavorpepinoflavor metabolitesmachine learning |
| spellingShingle | Zhu Sun Wenwen Zhao Yaping Li Cheng Si Xuemei Sun Qiwen Zhong Shipeng Yang An Exploration of Pepino (<i>Solanum muricatum</i>) Flavor Compounds Using Machine Learning Combined with Metabolomics and Sensory Evaluation Foods fruit flavor pepino flavor metabolites machine learning |
| title | An Exploration of Pepino (<i>Solanum muricatum</i>) Flavor Compounds Using Machine Learning Combined with Metabolomics and Sensory Evaluation |
| title_full | An Exploration of Pepino (<i>Solanum muricatum</i>) Flavor Compounds Using Machine Learning Combined with Metabolomics and Sensory Evaluation |
| title_fullStr | An Exploration of Pepino (<i>Solanum muricatum</i>) Flavor Compounds Using Machine Learning Combined with Metabolomics and Sensory Evaluation |
| title_full_unstemmed | An Exploration of Pepino (<i>Solanum muricatum</i>) Flavor Compounds Using Machine Learning Combined with Metabolomics and Sensory Evaluation |
| title_short | An Exploration of Pepino (<i>Solanum muricatum</i>) Flavor Compounds Using Machine Learning Combined with Metabolomics and Sensory Evaluation |
| title_sort | exploration of pepino i solanum muricatum i flavor compounds using machine learning combined with metabolomics and sensory evaluation |
| topic | fruit flavor pepino flavor metabolites machine learning |
| url | https://www.mdpi.com/2304-8158/11/20/3248 |
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